mlsanityData to 14 Oct 2026

Age and Power Minimization via Meta-Deep Reinforcement Learning in UAV Networks

arXiv 2501.14603 ↗First posted 24 Jan 2025cs.LG cs.AIcited by 2

On the map: Deep reinforcement learning methods → RL for robot navigation and UAV control

Abstract (arXiv)

Age-of-information (AoI) and transmission power are crucial performance metrics in low energy wireless networks, where information freshness is of paramount importance. This study examines a power-limited internet of things (IoT) network supported by a flying unmanned aerial vehicle(UAV) that collects data. Our aim is to optimize the UAV flight trajectory and scheduling policy to minimize a varying AoI and transmission power combination. To tackle this variation, this paper proposes a meta-deep reinforcement learning (RL) approach that integrates deep Q-networks (DQNs) with model-agnostic meta-learning (MAML). DQNs determine optimal UAV decisions, while MAML enables scalability across varying objective functions. Numerical results indicate that the proposed algorithm converges faster and adapts to new objectives more effectively than traditional deep RL methods, achieving minimal AoI and transmission power overall.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Placed in RL for robot navigation and UAV control, one of 15 directions in Deep reinforcement learning methods. Builds on 12 of its 40 references that are in the map. First posted on arXiv 24 Jan 2025.

Builds on

12 of 40 references.

References · 12

  1. 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,897
  2. 2018Applications of Deep Reinforcement Learning in Communications and Networking: A Surveycited by 167
  3. 2022Eco2AI: carbon emissions tracking of machine learning models as the first step towards sustainable AIcited by 58
  4. 2016Wireless Communications with Unmanned Aerial Vehicles: Opportunities and Challengescited by 43
  5. 2019Deep Reinforcement Learning Based Dynamic Trajectory Control for UAV-assisted Mobile Edge Computingcited by 16
  6. 2023Multi-UAV Path Learning for Age and Power Optimization in IoT with UAV Battery Rechargecited by 9
  7. 2022Traffic Prediction and Fast Uplink for Hidden Markov IoT Modelscited by 8
  8. 2020White Paper on Critical and Massive Machine Type Communication Towards 6Gcited by 3
  9. 2022Continual Meta-Reinforcement Learning for UAV-Aided Vehicular Wireless Networkscited by 3
  10. 2024Traffic Learning and Proactive UAV Trajectory Planning for Data Uplink in Markovian IoT Modelscited by 3
  11. 2024MetaGraphLoc: A Graph-based Meta-learning Scheme for Indoor Localization via Sensor Fusioncited by 3
  12. 2024Meta-Learning Based Optimization for Large Scale Wireless Systemscited by 1

Built on by

Cited by year

Citing papers per year: 2025: 2.

2 papers.

  1. 2025Resilient UAV Trajectory Planning via Few-Shot Meta-Offline Reinforcement Learningcited by 1
  2. 2025A Survey of Freshness-Aware Wireless Networking with Reinforcement Learningcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Optimizing Age of Trust and Throughput in Multi-Hop UAV-Aided IoT Networks
  2. 2025Age of Information Minimization in UAV-Enabled Integrated Sensing and Communication Systems
  3. 2026Optimizing Energy and Data Collection in UAV-aided IoT Networks using Attention-based Multi-Objective Reinforcement Learning
  4. 2025AoI-Sensitive Data Forwarding with Distributed Beamforming in UAV-Assisted IoT
  5. 2026AoI-Driven Hierarchical Learning for Cooperative Resource Sharing in Multi-Operator UAV Networks ↗
  6. 2025Multi-Agent Deep Reinforcement Learning for UAV-Assisted 5G Network Slicing: A Comparative Study of MAPPO, MADDPG, and MADQN ↗
  7. 2025Large Language Model-Empowered Decision Transformer for UAV-Enabled Data Collection
  8. 2025Sum Rate Maximization in STAR-RIS-UAV-Assisted Networks: A CA-DDPG Approach for Joint Optimization